Quickstart: Create a skillset in the Azure portal

2025-09-16

Important

The Import data (new) wizard now supports keyword search, which was previously only available in the Import data wizard. We recommend the new wizard for an improved search experience. For more information about how we're consolidating the wizards, see Import data wizards in the Azure portal.

In this quickstart, you learn how a skillset in Azure AI Search adds optical character recognition (OCR), image analysis, language detection, text merging, and entity recognition to generate text-searchable content in an index.

You can run the Import data (new) wizard in the Azure portal to apply skills that create and transform textual content during indexing. The input is your raw data, usually blobs in Azure Storage. The output is a searchable index containing AI-generated image text, captions, and entities. You can then query generated content in the Azure portal using Search explorer.

Before you run the wizard, you create a few resources and upload sample files.

Prerequisites

An Azure account with an active subscription. Create an account for free.
An Azure AI Search service. Create a service or find an existing service in your current subscription. You can use a free service for this quickstart.
An Azure Storage account. Use Azure Blob Storage on a standard performance (general-purpose v2) account. To avoid bandwidth charges, use the same region as Azure AI Search.

Note

This quickstart uses Azure AI services for AI enrichment. Because the workload is small, Azure AI services is tapped behind the scenes for free processing up to 20 transactions. Therefore, you don't need to create an Azure AI services multi-service resource.

Prepare sample data

In this section, you create an Azure Storage container to store sample data consisting of various file types, including images and application files that aren't full-text searchable in their native formats.

To prepare the sample data for this quickstart:

Sign in to the Azure portal and select your Azure Storage account.
From the left pane, select Data storage > Containers.
Create a container, and then upload the sample data to the container.

Run the wizard

To run the wizard:

Sign in to the Azure portal and select your search service.
On the Overview page, select Import data (new).
Select Azure Blob Storage for the data source.
Select Keyword search.

Step 1: Create a data source

Azure AI Search requires a connection to a data source for content ingestion and indexing. In this case, the data source is your Azure Storage account.

To create the data source:

On the Connect to your data page, select your Azure subscription.
Select your storage account, and then select the container you created.
Select Next.

If you get Error detecting index schema from data source, the indexer that powers the wizard can't connect to your data source. The data source most likely has security protections. Try the following solutions, and then rerun the wizard.

Security feature	Solution
Resource requires Azure roles, or its access keys are disabled.	Connect as a trusted service or connect using a managed identity.
Resource is behind an IP firewall.	Create an inbound rule for Azure AI Search and the Azure portal.
Resource requires a private endpoint connection.	Connect over a private endpoint.

Step 2: Add cognitive skills

The next step is to configure AI enrichment to invoke OCR, image analysis, and entity recognition.

OCR and image analysis are available for blobs in Azure Blob Storage and Azure Data Lake Storage (ADLS) Gen2 and for image content in Microsoft OneLake. Images can be standalone files or embedded images in a PDF or other files.

To add the skills:

Select Extract entities, and then select the gear icon.
Select and save the following checkboxes:
- Persons
- Locations
- Organizations
Select Extract text from images, and then select the gear icon.
Select and save the following checkboxes:
- Generate tags
- Categorize content
Leave the Use a free AI service (limited enrichments) checkbox selected.

The sample data consists of 14 files, so the free allotment of 20 transactions on Azure AI services is sufficient.
Select Next.

Step 3: Configure the index

An index contains your searchable content. The wizard can usually create the schema by sampling the data source. In this step, you review the generated schema and potentially revise any settings.

For this quickstart, the wizard sets reasonable defaults:

Default fields are based on metadata properties of existing blobs and new fields for the enrichment output, such as persons, locations, and organizations. Data types are inferred from metadata and by data sampling.
Default document key is metadata_storage_path, which is selected because the field contains unique values.
Default field attributes are based on the skills you selected. For example, fields created by the Entity Recognition skill (persons, locations, and organizations) are Retrievable, Filterable, Facetable, and Searchable. To view and change these attributes, select a field, and then select Configure field.

Retrievable fields can be returned in results, while Searchable fields support full-text search. Use Filterable if you want to use fields in a filter expression.

Marking a field as Retrievable doesn't mean that the field must appear in search results. You can control which fields are returned by using the select query parameter.

After you review the index schema, select Next.

Step 4: Skip advanced settings

The wizard offers advanced settings for semantic ranking and index scheduling, which are beyond the scope of this quickstart. Skip this step by selecting Next.

Step 5: Review and create objects

The last step is to review your configuration and create the index, indexer, and data source on your search service. The indexer automates the process of extracting content from your data source, loading the index, and driving skillset execution.

To review and create the objects:

Accept the default Objects name prefix.
Review the object configurations.

AI enrichment, semantic ranker, and indexer scheduling are either disabled or set to their default values because you skipped their wizard steps.
Select Create to simultaneously create the objects and run the indexer.

Monitor status

You can monitor the creation of the indexer in the Azure portal. Skills-based indexing takes longer than text-based indexing, especially OCR and image analysis.

To monitor the progress of the indexer:

From the left pane, select Indexers.
Select your indexer from the list.
Select Success (or Failed) to view execution details.

In this quickstart, there are a few warnings, including Could not execute skill because one or more skill input was invalid. This warning tells you that a PNG file in the data source doesn't provide a text input to Entity Recognition. It occurs because the upstream OCR skill didn't recognize any text in the image and couldn't provide a text input to the downstream Entity Recognition skill.

Warnings are common in skillset execution. As you become familiar with how skills iterate over your data, you might begin to notice patterns and learn which warnings are safe to ignore.

Query in Search explorer

To query your index:

From the left pane, select Indexes.
Select your index from the list. If the index has zero documents or storage, wait for the Azure portal to refresh.
On the Search explorer tab, enter a search string, such as satya nadella.

The search bar accepts keywords, quote-enclosed phrases, and operators. For example: "Satya Nadella" +"Bill Gates" +"Steve Ballmer"

Results are returned as verbose JSON, which can be hard to read, especially in large documents. Here are tips for searching in this tool:

Switch to the JSON view to specify parameters that shape results.
Add select to limit the fields in results.
Add count to show the number of matches.
Use Ctrl-F to search within the JSON for specific properties or terms.

Here's some JSON you can paste into the view:

{
"search": "\"Satya Nadella\" +\"Bill Gates\" +\"Steve Ballmer\"",
"count": true,
"select": "merged_content, persons"
}

Tip

Query strings are case sensitive, so if you get an "unknown field" message, check Fields or Index Definition (JSON) to verify the name and case.

Important

The Import data wizard will eventually be deprecated. Most of its functionality is available in the Import data (new) wizard, which we recommend for most search scenarios. For more information, see Import data wizards in the Azure portal.

You can run the Import data wizard in the Azure portal to apply skills that create and transform textual content during indexing. The input is your raw data, usually blobs in Azure Storage. The output is a searchable index containing AI-generated image text, captions, and entities. You can then query generated content in the Azure portal using Search explorer.

Before you run the wizard, you create a few resources and upload sample files.

Prerequisites

An Azure account with an active subscription. Create an account for free.
An Azure AI Search service. Create a service or find an existing service in your current subscription. You can use a free service for this quickstart.
An Azure Storage account. Use Azure Blob Storage on a standard performance (general-purpose v2) account. To avoid bandwidth charges, use the same region as Azure AI Search.

Note

Prepare sample data

To prepare the sample data for this quickstart:

Sign in to the Azure portal and select your Azure Storage account.
From the left pane, select Data storage > Containers.
Create a container, and then upload the sample data to the container.